With the proposed model, we aim to make two contributions.
First, we present a hierarchical predictive processing-based model for perception and action. This model is able to generate responsive, yet goal-directed behavior in an efficient and robust way. Crucially, it solves the planning problem without any explicit reasoning about or representation of the task structure (e.g., how exactly a specific meal needs to be prepared).
This is achieved by integrating bottom-up information, e.g., about current affordances for actions in the environment, with top-down information about the agent's goals. The integration yields the agent's current intentions and influences its actions and perception.
Second, we propose an implicit coordination strategy, called belief resonance, that rests on integrating beliefs about another agent's intentions or goals, with beliefs about one's own actions and intentions.
The degree to which another agent's intention influences one's own beliefs is controlled by a so-called susceptibility parameter (SP).
Using this parameter, different roles (e.g., leader or follower) can be realized by controlling how susceptible agents are to the mental states of another agent. In order to infer another's mental state efficiently, we integrate the active inference framework with a light-weight mentalizing approach based on a model of Bayesian Theory of Mind (BToM) [1]}.
